The Types of Data Visualizations - dummies

The Types of Data Visualizations

By Lillian Pierson

A data visualization is a visual representation that’s designed for the purpose of conveying the meaning and significance of data and data insights. Since data visualizations are designed for a whole spectrum of different audiences, different purposes, and different skill levels, the first step to designing a great data visualization is to know your audience.

Audiences come in all shapes, forms, and sizes. You could be designing something for the young and edgy readers of Rolling Stone magazine, or perhaps you need to design a visualization to convey scientific findings to a research group. It’s possible that your audience is comprised of board members and organizational decision makers, or perhaps you’re designing a piece that’s meant to stir up a ruckus with members of a local grassroots organization.

Since each audience will be comprised of a unique class of consumers, each with their unique data visualization needs, it’s essential to clarify exactly for whom you’re designing. In the paragraphs, you get to know the three main types of data visualizations and how to pick the one that best meets your audience’s needs.

Data storytelling for organizational decision makers

Sometimes you have to design data visualizations for a less-technical audience, perhaps in order to help members of this audience make better-informed business decisions. The purpose of this type of visualization is to tell your audience the story behind the data. In data storytelling, the audience depends on you to make sense of the data behind the visualization and then turn useful insights into visual stories that they can understand.

With data storytelling, your goal should be to create a clutter-free, highly focused visualization so that members of your audience can quickly extract meaning without much effort. These visualizations are best delivered in the form of static images, but more adept decision makers may prefer to have an interactive dashboard that they can use to do a bit of exploration and what-if modeling.

Data showcasing for analysts

If you’re designing for a crowd of logical, calculating analysts, you can create data visualizations that are rather open-ended. The purpose of this type of visualization is to help audience members visually explore the data and draw their own conclusions.

When using data showcasing techniques, your goal should be to display a lot of contextual information that supports your audience members in making their own interpretations. These visualizations should include more­contextual data and less conclusive focus, so people can get in there, analyze the data for themselves, and draw their own conclusions. These visualizations are best delivered as static images or dynamic, interactive dashboards.

Designing data art for activists

You could be designing for an audience of idealists, dreamers, and change-makers. When designing for this audience, you want your data visualization to make a point! You can assume that your typical audience member isn’t that analytical. What these people lack in math skills, however, they more than compensate for in solid convictions.

These people look to your data visualization as a vehicle by which to make a statement. When designing for this audience, data art is the way to go. The main goal in data art is to entertain, to provoke, to annoy, or to do whatever it takes to make a loud, clear, attention-demanding statement. Data art has little to no narrative and doesn’t offer room for viewers to form their own interpretations.

It’s important to emphasize here that data scientists have an ethical responsibility to always represent data accurately. A data scientist should never distort the message of the data to fit what the audience wants to hear — not even for data art! Non-technical audiences won’t even know what the possible issues are, let alone be able to see them. They rely on the data scientist to provide honest and accurate representations, thus amplifying the level of ethical responsibility that the data scientist must assume.